US8527303B2 - Automated processing of medical data for disability rating determinations - Google Patents

Automated processing of medical data for disability rating determinations Download PDF

Info

Publication number
US8527303B2
US8527303B2 US12/603,561 US60356109A US8527303B2 US 8527303 B2 US8527303 B2 US 8527303B2 US 60356109 A US60356109 A US 60356109A US 8527303 B2 US8527303 B2 US 8527303B2
Authority
US
United States
Prior art keywords
medical
queries
claimant
disability
medical evidence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
US12/603,561
Other versions
US20100106520A1 (en
Inventor
Lay K. Kay
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
QTC Management Inc
Original Assignee
QTC Management Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by QTC Management Inc filed Critical QTC Management Inc
Priority to US12/603,561 priority Critical patent/US8527303B2/en
Publication of US20100106520A1 publication Critical patent/US20100106520A1/en
Assigned to QTC MANAGEMENT, INC. reassignment QTC MANAGEMENT, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: KAY, LAY K.
Application granted granted Critical
Publication of US8527303B2 publication Critical patent/US8527303B2/en
Assigned to CITIBANK, N.A. reassignment CITIBANK, N.A. SECURITY INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ABACUS INNOVATIONS TECHNOLOGY, INC., LOCKHEED MARTIN INDUSTRIAL DEFENDER, INC., OAO CORPORATION, QTC MANAGEMENT, INC., REVEAL IMAGING TECHNOLOGIES, INC., Systems Made Simple, Inc., SYTEX, INC., VAREC, INC.
Assigned to CITIBANK, N.A. reassignment CITIBANK, N.A. SECURITY INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ABACUS INNOVATIONS TECHNOLOGY, INC., LOCKHEED MARTIN INDUSTRIAL DEFENDER, INC., OAO CORPORATION, QTC MANAGEMENT, INC., REVEAL IMAGING TECHNOLOGIES, INC., Systems Made Simple, Inc., SYTEX, INC., VAREC, INC.
Assigned to QTC MANAGEMENT, INC., OAO CORPORATION, Systems Made Simple, Inc., VAREC, INC., LEIDOS INNOVATIONS TECHNOLOGY, INC. (F/K/A ABACUS INNOVATIONS TECHNOLOGY, INC.), REVEAL IMAGING TECHNOLOGY, INC., SYTEX, INC. reassignment QTC MANAGEMENT, INC. RELEASE BY SECURED PARTY (SEE DOCUMENT FOR DETAILS). Assignors: CITIBANK, N.A., AS COLLATERAL AGENT
Assigned to OAO CORPORATION, Systems Made Simple, Inc., REVEAL IMAGING TECHNOLOGY, INC., VAREC, INC., SYTEX, INC., QTC MANAGEMENT, INC., LEIDOS INNOVATIONS TECHNOLOGY, INC. (F/K/A ABACUS INNOVATIONS TECHNOLOGY, INC.) reassignment OAO CORPORATION RELEASE BY SECURED PARTY (SEE DOCUMENT FOR DETAILS). Assignors: CITIBANK, N.A., AS COLLATERAL AGENT
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computing arrangements using knowledge-based models
    • G06N5/02Knowledge representation; Symbolic representation
    • G06N5/022Knowledge engineering; Knowledge acquisition
    • G06N5/025Extracting rules from data
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/10Office automation; Time management
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08Insurance

Definitions

  • the present invention relates to methods and systems for gathering and processing medical data to support rating decisions in the adjudication of insurance and disability requests.
  • Medical evidence requirements refers to requirements of information about a claimant that is relevant to the medical conditions claimed by the claimant, such as the age and gender of the claimant, physical examination data, laboratory test data and medical history data pertinent to the claims, and so forth.
  • the requirements are specified by rules developed by the government agency or by the insurance company, pertinent case law, government regulations, legislation and administrative decisions, and so forth.
  • the requirements may specify that if a claimant claims a certain medical condition, a medical provider must conduct certain physical examinations and laboratory tests on the claimant or ask certain questions.
  • the requirements may also specify, for example, that a claimant must have a range of motions less than a certain degree to claim a limb disability.
  • Requirements can also be specified by conventional medical knowledge, for example requiring a certain test to confirm a particular claimed condition.
  • the rating rules are normally documented in manuals that may have many different titles, herein referred to as “rating books.”
  • a rating code refers to a classification used by the government agency or insurance company that typically refers to a medical condition or a class of medical conditions in a rating book.
  • the collection of rating rules, rating codes, pertinent legislation and case law for an insurance or disability program is herein referred to as the “rules collection” for that program.
  • the rating rules may include rules on how to make a rating decision based on the collected medical evidence and the rating codes. For example, in a V.A. disability program, the rules collection typically specifies a disability percentage range based on rating codes and collected medical evidence. A V.A. rating personnel reviews the rating codes and medical evidence, and specifies a disability percentage within the range.
  • the claimant In a disability or insurance request process, the claimant typically visits a hospital, clinic or medical office.
  • a medical provider such as a physician or a nurse collects medical evidence from the claimant to support a rating decision.
  • the rating decision is typically made by the government agency or the insurance company based on the medical evidence collected by the medical provider and based on the rules collection.
  • the medical providers are typically provided with documents generally referred to as “physician's disability evaluation” or “medical examination handbooks” to assist them with collecting medical evidence.
  • the handbooks are herein referred to as “medical handbooks.”
  • the medical handbooks typically contain the medical evidence requirements for the rules collection.
  • the rating books are typically intended for the rating personnel in the government agency or insurance company
  • the medical handbooks are typically intended for the medical providers. Although they are somehow related, the rating books and medical handbooks typically contain very few direct cross-references.
  • the medical providers often are not familiar with the rules collection of the insurance or disability program, and make mistakes in using the medical handbooks. Therefore, the required medical evidence can be omitted or entered incorrectly, thus affecting the making of a correct rating decision.
  • the rating personnel who typically have only limited medical knowledge, must spend considerable time to review the medical information collected by the medical providers. What is desired is an automated system that provides instructions to medical providers to collect medical evidence based on the rules collection of the insurance or disability program. What is also desired is a system that provides supporting information in a user-friendly format to assist rating personnel in making a rating decision based on the collected medical evidence.
  • the VA Compensation and Pension (C&P) program is described as an example.
  • This government program provides payments of benefits to military veterans for medical disability resulting from their military service.
  • the rating rules are included in the Code of Federal Regulations 38-CFR, the governing legislation, and in a rating book.
  • the related medical handbook is a series of documents titled Automatic Medical Information Exchange (AMIE) worksheets. These worksheets specify the medical evidence required and the procedures to be utilized for each claimed condition included in 38-CFR.
  • AMIE Automatic Medical Information Exchange
  • One aspect of the invention relates to a method of assisting the collection of medical evidence for the adjudication of a medical insurance or disability request.
  • One or more claims of medical conditions are received from a claimant.
  • a plurality of claimant-specific medical evidence queries are generated.
  • a plurality of instructions are generated based on the medical evidence queries. The instructions are then used to collect the required medical evidence from physical exams, laboratory tests, medical records or claimant questionnaires.
  • Another aspect of the invention relates to a method of assisting the adjudication of a medical insurance or disability request.
  • One or more claims of medical conditions are received from a claimant.
  • Medical evidence queries are generated based on the received claim and based on a disability rules collection. Each query is preferably associated with a rating code of the rules collection. Medical evidence is then collected from the claimant according to the generated queries.
  • a rating report that displays the collected medical evidence is created to assist rating personnel in making a rating decision.
  • the rating report also preferably displays the rating codes associated with the displayed medical evidence.
  • Still another embodiment relates to a medical disability claims benefits system. It includes a rules mapping component, a knowledge library, a claimant-specific queries creation component and a protocol creation component.
  • the rules mapping component organizes a medical disability rules collection into at least a first plurality of general medical evidence queries.
  • the knowledge library stores the first plurality of general medical evidence queries.
  • the claimant-specific query creation selects from the first plurality of general queries stored in the knowledge library a second plurality of claimant-specific medical evidence queries based on at least one claimed medical condition of a claimant.
  • the protocol creation component creates at least one data collection protocol based on the second plurality of queries.
  • the system can also include a report creation component, which receives medical evidence collected according to the at least one data collection protocol, and displays at least a portion of the received medical evidence.
  • FIG. 1 illustrates the general overview of one embodiment of a disability benefits claims system.
  • FIG. 2 illustrates one embodiment of an arrangement of modules and sub-modules.
  • FIG. 3 illustrates one embodiment of an arrangement of categories and sub-categories.
  • FIG. 4 illustrates one embodiment of a process of organizing rules collection into a knowledge library.
  • FIG. 5 illustrates one embodiment of a process of generating claimant-specific medical evidence queries.
  • FIG. 6 illustrates one embodiment of a data entry form for claimed medical conditions.
  • FIGS. 7A-7D illustrate one embodiment of a medical provider's exam protocol.
  • FIGS. 8A-8E illustrate one embodiment of a claimant questionnaire.
  • FIGS. 9A-9B illustrate one embodiment of a narrative medical report.
  • FIG. 10 illustrates one embodiment of a diagnostic code summary medical report.
  • FIGS. 11A-11H illustrate one embodiment of a rating report.
  • FIG. 1 illustrates the general overview of one embodiment of a disability benefits claims system 1 .
  • the rules collection 112 for the insurance or disability program and pertinent medical knowledge 114 are organized by a FID mapping component 116 into a knowledge library 1 .
  • the claimant-specific query creation module 122 creates claimant-specific medical evidence queries.
  • the queries are then separated into medical record queries 124 for medical records, and exam queries 126 for physical exams and laboratory tests.
  • the medical record queries 124 for medical records are used by the clerk's protocol creation component 134 to create a clerk's data collection protocol to collect the required data from medical records.
  • the exam queries 126 for exams and tests are used by the medical provider's exam protocol creation component 136 to create a medical provider's data collection protocol to assist a physician, nurse or technician in conducting physical exams or laboratory tests.
  • the component 136 may also use the exam queries 126 to create a claimant's questionnaire to be answered by the claimant.
  • the medical report creation component 142 uses the medical evidence collected from exams, tests, claimant questionnaire and medical records to create a medical report.
  • the rating report creation component 152 creates a rating report to assist rating personnel in adjudicating the claims.
  • the collected medical evidence can be stored in a claimant database 160 .
  • component 112 represents the rules collection for the insurance or disability program, typically embodied in rating books, legislation, administrative decisions and case law.
  • Component 114 represents pertinent medical knowledge, such as instructions to a physician, lab technician or nurse for performing a physical exam or laboratory test. The rules collection and medical knowledge are organized by a FID mapping component 116 into FIDs and stored in a knowledge library component 118 .
  • every unit of data that may be required by the rules collection for making a rating decision is identified by a field identification number (FID).
  • FID data fields include a “patient name” field, a “heart rate” field, a “impaired limb motion range” field, and so forth.
  • Each general medical evidence query is identified by a FID.
  • a general medical evidence query corresponds to a medical evidence requirement specified by the rules collection or by medical knowledge.
  • a claimant-specific medical evidence query is generated from the general medical evidence queries and based on the claimant's claimed medical conditions. Claimant-specific queries are described in the subsection titled “Generating claimant-specific medical evidence queries”.
  • each FID includes a category code, a rating code and a data query code, separated by the underline symbol “_”.
  • a FID can take the form of “H047_SM500_T001”.
  • the category code “H047” identifies the FID to a category of queries concerning the right knee.
  • the rating code “SM500” identifies the FID to a particular rating code for musculoskeletal injuries in a rating book.
  • the data query code “T001” identifies the FID to the data query “What is the range of motion?.”
  • the FID mapping number is “TK10_TS6600_T001”.
  • the category code “TK10” represents a category of queries concerning bronchitis.
  • the rating code “TS6600” represents a rating book rating code “6600”.
  • the data query code “T001” represents the query “What is the FEV1 value?.”
  • a query text table stores the data query codes and the query text for each of the data query codes.
  • the table may also store a long instruction text for each data query code as an instruction or explanation.
  • the stored query text and long instruction text can be later displayed in a medical provider's exam protocol, claimant questionnaire, clerk's data collection protocol, medical report or rating report.
  • a FID can take other forms.
  • a rating code table can store the rating code for each data query code
  • a category code table can store the category code for each data query code. Therefore a FID need only include a data query code, and the rating code and category code for the FID can be identified by referencing the rating code table and the category code table.
  • a FID can be an object that includes a data query object field, a rating code object field and a category code object field.
  • the FID mapping component 116 organizes the rules collection into a plurality of FIDs. For example, for a rating code that identifies diabetes in a V.A. rules collection, the component 116 creates a plurality of FIDs, with each FID identifying a unit of medical evidence required for making a rating decision on the diabetes claim.
  • Each FID preferably includes a category code, the V.A. rating code that identifies diabetes, and a data query code.
  • one FID includes a data query code representing the data query “Have you served in the Vietnam War?” because V.A. rules assume that Vietnam veterans' diabetes conditions are caused by exposure to Agent Orange.
  • the data query code may be further associated with a long instruction text “If claimant has served in Vietnam and suffers from diabetes, assume that service connection exists.”
  • FIG. 2 illustrates one embodiment of a disability benefits claims systems 100 that includes modules and sub-modules.
  • a rating book typically classifies medical conditions into disease systems, also called body systems. Typical disease systems may include the cardiovascular system, the respiratory system, infectious diseases, and so forth. Some rating books classify a disease system into one or more sub-disease systems or sub-body systems.
  • a “cardiovascular disease system” may include sub-disease systems such as myocardial-infarction sub-disease system, arrhythmia sub-disease system, and so forth.
  • a sub-disease system is typically unique to one disease system and is not shared by multiple disease systems.
  • each sub-disease system is mapped to one or more modules of the disability benefits claims system 1 .
  • a module represents a function within the sub-disease system.
  • the lung sub-disease system can be mapped to a “history of symptoms” module, a “history of general health” module, a “physical examination of the lungs” module, and so forth.
  • sub-disease systems can share common modules.
  • modules are assigned priority numbers that identify a priority order among the modules.
  • Each module can include one or more sub-modules.
  • a “vital signs” sub-module can include data about the height, weight, pulse, and blood pressure of the claimant.
  • a sub-module includes one or more FIDs.
  • Modules can share common sub-modules.
  • the “vital signs” sub-module can be shared by multiple modules because vital signs information is needed for the diagnosis of many diseases and conditions.
  • sub-modules are assigned priority numbers that identify a priority order among the sub-modules.
  • a sub-module includes one or more FIDs.
  • the “vital signs” sub-module includes a “height” FID, a “weight” FID, a “pulse” FID and a “blood pressure” FID.
  • each FID belongs to only one sub-module.
  • each FID includes a sub-module code that identifies the sub-module of the FID.
  • a sub-module table in the knowledge library 118 stores the FIDs for each sub-module.
  • Each rating code and its general medical evidence queries directly correspond to a collection of FIDs.
  • the FID collections for two rating codes may share one or more FIDs.
  • the unique data elements that make up the rules collection are grouped by category and sub-category.
  • the categories and sub-categories preferably relate to classifications in the rating books.
  • categories can include “General”, “Complications”, “Function”, “Symptoms”, “Tests”, and so forth.
  • a category can be further classified into one or more sub-categories.
  • the “Function” category includes the sub-categories “ability” and “restriction”.
  • the “Tests” category can include sub-categories “confirmation,” “essential,” “indication,” and “results.”
  • a sub-category includes one or more FIDs.
  • FIG. 4 illustrates one embodiment of a process of organizing rules collection into FIDs. From a start block 410 , the process proceeds to a block 420 to identify rating codes from the rating books for the disability or insurance program. The process then proceeds to a block 430 to identify data fields within each rating code. Each data field represents a general medical evidence query. Data fields may also be identified based on pertinent medical knowledge, for example the knowledge of a experienced physician that certain medical evidence are needed to make a rating decision for a particular rating code. Data fields may also be identified based on case law and administrative decisions, for example the Deluca case and required “Deluca issues.”
  • the process then proceeds to a block 440 to group the data fields by category. In another embodiment, data fields are grouped by sub-category.
  • the process proceeds to a block 450 , where a FID is assigned to each data field.
  • a category code, a rating code and a data query code is assigned to each FID.
  • the category code represents the category the data field is grouped into.
  • the rating code represents the rating code for the data field.
  • the data query code represents the data query for the medical evidence query.
  • the process then proceeds to a block 460 to store the FIDs in a knowledge library component 1 .
  • the process terminates at an end block 470 .
  • the claimant-specific query creation module 122 receives the claimed medical conditions 120 from the claimant, and creates claimant-specific medical evidence query based on the claims and by referring to the general medical evidence queries stored in the knowledge library 1 .
  • FIG. 5 illustrates one embodiment of the query-creation process.
  • the process starts from a start block 510 and proceeds to a block 520 , where the query creation component 122 receives one or more claims of medical conditions from the claimant.
  • the component 122 also receives other information provided by the claimant, for example information such as claimant name, age, gender filled out by the claimant on a data entry form form.
  • FIG. 6 is an example data entry form. It can be filled out by the claimant or by a clerk.
  • the “Special Instructions to the Doctor” section displays special instructions retrieved from the knowledge library 118 for the particular insurance or disability program and displayed as a reminder to the medical provider.
  • the component 122 identifies the related modules based on the received claims. For example, if the claimed condition is “loss of eyesight,” the component 122 may identify a “physical exam” module and a “neurological exam” module. The relationships of medical conditions and related modules are stored in the knowledge library 1 . The component 122 also identifies all sub-modules of the identified modules. If two of the identified modules share common sub-modules, the duplicate sub-modules with the lower priority numbers are removed. From all of the FIDs that belong to the identified modules, the duplicate FIDs can also be removed.
  • the component 122 instead of identifying the related modules based on the received claims, the component 122 identifies the related sub-modules, the related categories, or the related sub-categories. In another embodiment, the component 122 directly identifies the related FIDs stored in the knowledge library 118 based on the received claims.
  • the component 122 selects those FIDs in the knowledge library 118 that belong to the identified modules and sub-modules.
  • the selected FIDs form a set of the claimant-specific medical evidence queries.
  • the set can be stored in a variety of formats, for example as a text string with FIDs separated by field delimiters such as colons or semicolons, as a text file with a FID in each line, as a table with each FID as a record, as a series of objects with each FID having a “next FID” pointer that points to the next FID object, and so forth.
  • the component 122 compares the information already received from the claimant, and fills the related FIDs with such information. For example, if the claimant has provided his or her name, age and gender, the component then fills the related FIDs with the claimant-provided information. The details of filling a FID with collected medical evidence are described below in more detail.
  • the process proceeds to a block 550 , where the component 122 determines which of the generated claimant-specific queries may be satisfied from medical records.
  • a human operator reviews the generated queries and determines which of the queries may be satisfied from medical records.
  • the determination instead of determining on a per FID basis, the determination can also be made on a per module, per sub-module, per category or per sub-category basis.
  • the process proceeds to a block 5 . Otherwise the process proceeds to a block 560 , where the component 122 generates a set of claimant-specific queries to be satisfied from physical exams, claimant questionnaires, or laboratory tests.
  • the component 122 generates a set of queries whose results can be obtained from existing medical records.
  • the claimant-specific queries generated at the block 540 are thus separated into two sets of queries.
  • the queries generated at the block 540 are separated into three sets: one set of queries to be satisfied from physical exams and claimant questionnaires, another set to be satisfied from laboratory tests, and a third set to be satisfied from medical records.
  • the component 122 searches for duplicate modules and eliminates such duplications. In other embodiments, the component can also search for and eliminate duplications on the sub-module or FID level.
  • Each module is associated with a priority number stored in the knowledge library 1 . In the case where multiple modules are called that examine the same sub-disease system, the duplicate modules with the lower priority numbers are eliminated. In another embodiment, each FID is associated with a priority number stored in the knowledge library 118 .
  • the generated queries can be updated by a human operator.
  • a human operator For example, a medical provider or rating personnel reviews the generated claimant-specific queries and adds, modifies or deletes one or more queries. This allows some flexibility and human control in the system 1 .
  • the human operator can also change the order of generated claimant-specific queries determined by the priority numbers.
  • the component 122 also checks special rules stored in the knowledge library 118 for exceptions and updates. Exceptions and updates are typically caused by changes in legislation, case law, and insurance or disability program rules. For example, special rules that represent the Deluca case decision can be stored in the knowledge library 1 . The stored Deluca special rules can be associated with FIDs, categories or modules stored in the knowledge library 1 . When the generated claimant-specific queries include a FID associated with a special rule, the special rule is retrieved from the knowledge library 118 and applied to include a special rule instruction with the FID, or to add, modify or remove other claimant-specific queries. The special rule can also change the order of generated claimant-specific queries determined by the priority numbers.
  • the medical provider's protocol creation component 136 creates a medical provider's data collection protocol, also called a physician's exam protocol.
  • the component 136 may also create a claimant questionnaire based on the claimant-specific queries.
  • the clerk's protocol creation component 134 creates a clerk's data collection protocol.
  • FIGS. 7A-7D illustrate an example medical provider's data collection protocol.
  • the protocol lists the claimant-specific medical evidence queries to be satisfied from physician exams and laboratory tests.
  • the queries are grouped by category and sub-category.
  • the category “PHYSICIAL EXAMINATION” shown in FIG. 7A includes sub-categories “VITAL SIGNS”, “HEENT”, “EYES”, “SKIN”, “HEART”, and “MUSCULOSKELETAL SYSTEM”.
  • the grouping of categories and sub-categories presents the queries in a user-friendly order to the medical provider.
  • the medical provider uses the exam protocol to examine the claimant, and preferably enters collected medical evidence into the protocol.
  • the exam protocol is displayed to the medical provider on the screen of an electronic device such as a computer or a personal digital assistant, and the medical provider enters the collected medical evidence corresponding to each query into the electronic device.
  • the exam protocol is displayed to the medical provider in a paper report, and the medical provider enters the collected medical evidence on the paper report for a clerk to enter into a computer system.
  • the medical evidence collected by the medical provider is then stored into the disability claims benefits system 1 .
  • the corresponding medical evidence is simply inserted into the end of the FID. For example, for the FID “H047_SM500_T001” described above, if the medical provider determines that the range of motion is 90 degrees, then the FID becomes “H047_SM500_T001 — 90”, with the last field within the FID storing the value of the medical evidence.
  • a table includes a “original FID” field that stores the FID of each query, and a “data value” that stores the medical evidence value of the corresponding FID. Other embodiments can also be implemented.
  • the component 136 may create a claimant questionnaire for those queries that can be satisfied by collecting answers directly from the claimant.
  • FIGS. 8A-8E illustrate an example claimant questionnaire.
  • the questionnaire can be filled out in paper or electronic form, by the claimant or by a clerk assisting the claimant.
  • the questionnaire displays generated claimant-specific queries that can be satisfied by collecting answers from the claimant.
  • the data entered into the questionnaire is then stored as collected medical evidence corresponding to the displayed queries.
  • the data can be stored with the FIDs as described above, and preferably displayed to the physician for review or verification.
  • the clerk's data collection protocol displays generated claimant-specific queries that are to be collected from medical records. For each query, the protocol preferably displays an instruction to the clerk, for example “retrieve data from previous x-ray charts.”
  • the instructions can be retrieved from instructions stored in the knowledge library 118 that are associated with stored general medical evidence queries.
  • the disability benefits claims system is connected to an electronic data storage that stores existing medical records, for example the claimant database 1 . The system then automatically searches the storage and collects required medical evidence from the storage. In another embodiment, the system automatically notifies a custodian of medical records via email, voice mail or paper report to search for the medical evidence specified by the queries.
  • the query creation component 122 may create conditional claimant-specific queries. For example, if a required exam reveals an abnormal condition, then additional medical evidence may be required according to the rules collection 112 or according to medical knowledge 1 . Such additional medical evidence queries are called conditional queries.
  • conditional queries are called conditional queries.
  • the query whose medical evidence may trigger the conditional queries is called a triggering query.
  • a triggering query may be associated with one or more sets of conditional queries. For example, a positive result of a laboratory test for a triggering query requires a first set of conditional queries, and a negative result may require a second set of conditional queries.
  • the FID of a triggering query stored in the knowledge library 118 includes a list of the FIDs of the conditional queries.
  • each query is stored as an object in the knowledge library 118 , and a triggering query object includes pointers to point to its conditional query objects.
  • the FID of a triggering query includes a flag code to indicate it is a triggering query.
  • a triggering query table includes a first field that stores the FID of a triggering query and a second field that stores the FIDs of the corresponding conditional queries.
  • the knowledge library 118 may also store a triggering rule that indicates under what conditions the conditional queries are needed, for example “when the triggering query returns a positive test result” or “when the triggering query's medical evidence is not available.”
  • the conditional queries for the triggering query are preferably also generated as claimant-specific queries.
  • the protocol creation components 134 and 136 identifies a triggering query, and preferably displays its corresponding conditional queries immediately following the triggering query.
  • the medical provider's exam protocol, clerk's data collection protocol and claimant's questionnaire preferably include instructions to explain the triggering rules, for example “if this test result is positive, then answer the following questions.”
  • the conditional queries can be displayed after the medical evidence for the triggering query is collected. For example, a medical provider's exam protocol is displayed to the medical provider on the screen of an electronic device, and the medical provider enters collected medical evidence into the electronic device. As the medical provider enters the medical evidence for a triggering query into the electronic device, the system 100 compares the entered medical evidence with the triggering query's triggering rule stored in the knowledge library 118 , and displays the conditional queries according to the triggering rule. If the conditional queries are to be collected from physical exams, they are displayed on the electronic device or on an additional paper report. The conditional queries can also be displayed on a claimant questionnaire or clerk's data collection protocol, in electronic or paper form.
  • FIGS. 9A-9B and FIG. 10 illustrate two example medical reports.
  • FIGS. 9A-9B illustrate a sample narrative report. It includes collected medical evidence, for example medical history data and other data, in preferably a narrative form.
  • FIG. 10 illustrates a sample diagnostic code summary report.
  • the report displays a summary of claimant-specific queries and medical evidences, and corresponding rating codes such as “5010” and “5003”.
  • the FID for each query includes a category code, a rating code and a data query code.
  • the rating code of the FID is thus displayed along with the collected medical evidence of the query.
  • the report thus displays direct relationships of medical conditions, medical evidence and rating codes.
  • the medical report can be used by medical providers to review the claimant's medical evidence and to familiarize the medical providers with the associated rating codes.
  • the report can also be used by rating personnel to review the claimant's medical evidence and associated rating codes.
  • medical reports can be used interchangeably with rating reports, which are described below in connection with FIGS. 11A-11H .
  • reports of different formats can be generated to conform to the commonly accepted format of the particular program.
  • the medical evidence queries can be grouped by disease system on a report for a first insurance program, and grouped by module on another report for a second disability program.
  • the rating report creation component 152 creates a rating report to assist rating personnel to adjudicate the insurance or disability requests of the claimant.
  • FIGS. 11A-11H illustrate an example rating report, also called a rating decision toolkit.
  • the rating report creation component 152 also recommends a rating decision to the rating personnel.
  • the rating decision can be generated based on a set of mathematical formulas, a rule-based system, an expert system, a self-learning neural network, a fuzzy logic system, and so forth.
  • the recommended rating decision can be generated in the form of a numerical value representing a disability percentage, a numerical value representing the insurance benefits dollar amount, a binary value representing a decision to grant or deny an insurance request, and so forth.
  • the component 152 recommends a V.A. rating of “10”, i.e., a disability percentage of 10%.
  • FIG. 11G displays a summary of all rating codes and corresponding recommended disability percentages, and a recommended combined disability percentage.
  • the rating personnel can review the rating report and accept, reject or modify the recommended ratings.
  • the disclosed disability claims benefits system 100 can be implemented in a variety of computer languages, commercial applications and operating platforms.
  • the system can be implement in whole or in part in Visual Basis, C, SQL, and so forth.

Abstract

Methods and systems are described for automated processing of medical data for insurance and disability determinations. Based on medical conditions claimed by the claimant, medical evidence queries are automatically generated to provide instructions to medical providers for conducting physical exams and laboratory tests and for retrieving medical records. After medical evidence is collected according to the queries, the medical evidence and related rating codes and decisions are displayed to rating personnel in a user-friendly format to assist in making a rating decision.

Description

REFERENCE TO RELATED APPLICATIONS
The present application is a continuation of and claims priority benefit under 35 U.S.C. §120 from U.S. patent application Ser. No. 10/279,759, filed Oct. 23, 2002, which claims priority benefit under 35 U.S.C. §119(e) from U.S. Provisional Application No. 60/344,663, filed Oct. 25, 2001, and U.S. Provisional Application No. 60/345,998, filed Oct. 24, 2001.
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to methods and systems for gathering and processing medical data to support rating decisions in the adjudication of insurance and disability requests.
2. Description of the Related Art
Government agencies and insurance companies have developed rules for adjudication of insurance or disability requests. Examples of insurance or disability programs include the Department of Veterans Affairs (VA) program, the Social Security Disability Insurance program, the Workers' Compensation program, various property and casualty insurance programs, and so forth.
In order to adjudicate a request made by a claimant, certain medical evidence is required. Medical evidence requirements refers to requirements of information about a claimant that is relevant to the medical conditions claimed by the claimant, such as the age and gender of the claimant, physical examination data, laboratory test data and medical history data pertinent to the claims, and so forth. The requirements are specified by rules developed by the government agency or by the insurance company, pertinent case law, government regulations, legislation and administrative decisions, and so forth. For example, the requirements may specify that if a claimant claims a certain medical condition, a medical provider must conduct certain physical examinations and laboratory tests on the claimant or ask certain questions. The requirements may also specify, for example, that a claimant must have a range of motions less than a certain degree to claim a limb disability. Requirements can also be specified by conventional medical knowledge, for example requiring a certain test to confirm a particular claimed condition.
The rating rules are normally documented in manuals that may have many different titles, herein referred to as “rating books.” A rating code refers to a classification used by the government agency or insurance company that typically refers to a medical condition or a class of medical conditions in a rating book. The collection of rating rules, rating codes, pertinent legislation and case law for an insurance or disability program is herein referred to as the “rules collection” for that program. The rating rules may include rules on how to make a rating decision based on the collected medical evidence and the rating codes. For example, in a V.A. disability program, the rules collection typically specifies a disability percentage range based on rating codes and collected medical evidence. A V.A. rating personnel reviews the rating codes and medical evidence, and specifies a disability percentage within the range.
In a disability or insurance request process, the claimant typically visits a hospital, clinic or medical office. A medical provider such as a physician or a nurse collects medical evidence from the claimant to support a rating decision. The rating decision is typically made by the government agency or the insurance company based on the medical evidence collected by the medical provider and based on the rules collection. The medical providers are typically provided with documents generally referred to as “physician's disability evaluation” or “medical examination handbooks” to assist them with collecting medical evidence. The handbooks are herein referred to as “medical handbooks.” The medical handbooks typically contain the medical evidence requirements for the rules collection.
Whereas the rating books are typically intended for the rating personnel in the government agency or insurance company, the medical handbooks are typically intended for the medical providers. Although they are somehow related, the rating books and medical handbooks typically contain very few direct cross-references. In addition, the medical providers often are not familiar with the rules collection of the insurance or disability program, and make mistakes in using the medical handbooks. Therefore, the required medical evidence can be omitted or entered incorrectly, thus affecting the making of a correct rating decision. In addition, the rating personnel, who typically have only limited medical knowledge, must spend considerable time to review the medical information collected by the medical providers. What is desired is an automated system that provides instructions to medical providers to collect medical evidence based on the rules collection of the insurance or disability program. What is also desired is a system that provides supporting information in a user-friendly format to assist rating personnel in making a rating decision based on the collected medical evidence.
In many cases, a claimant makes claims for multiple medical conditions. The conventional practice is to complete a medical evidence document for each claimed condition. This results in significant duplication of effort as duplicate medical data is gathered and identical medical procedures might be conducted multiple times. Therefore, what is desired is a system that eliminates the duplications.
To better illustrate the drawbacks of conventional practices and the need for better systems, the VA Compensation and Pension (C&P) program is described as an example. This government program provides payments of benefits to military veterans for medical disability resulting from their military service. The rating rules are included in the Code of Federal Regulations 38-CFR, the governing legislation, and in a rating book. The related medical handbook is a series of documents titled Automatic Medical Information Exchange (AMIE) worksheets. These worksheets specify the medical evidence required and the procedures to be utilized for each claimed condition included in 38-CFR. There are currently over fifty separate AMIE worksheets covering a wide array of claims, from a Prisoner of War Protocol Examination to Scars Examination. Each worksheet is designed as a stand-alone medical document for the particular claimed disability. In addition to the AMIE worksheets, legislatively mandated requirements, administrative requirements, and court ordered information have, from time to time, specified other medical evidence or dictated the manner in which it is to be collected. Significant training and experience is required to familiarize medical providers with the worksheets and the additional requirements. Significant delays and extra cost in claims processing are encountered when required medical evidence is not provided or incorrect procedures are conducted. Additionally, the claimant frequently claims multiple disabilities. These can number up to twenty or more claims for one claimant. The current practice is to complete an AMIE worksheet with all the requirements for each claimed disability. This results in unnecessary duplication of procedures with the entailed extra costs and time.
SUMMARY OF THE INVENTION
One aspect of the invention relates to a method of assisting the collection of medical evidence for the adjudication of a medical insurance or disability request. One or more claims of medical conditions are received from a claimant. Based on the received claims and based on a rules collection, a plurality of claimant-specific medical evidence queries are generated. A plurality of instructions are generated based on the medical evidence queries. The instructions are then used to collect the required medical evidence from physical exams, laboratory tests, medical records or claimant questionnaires.
Another aspect of the invention relates to a method of assisting the adjudication of a medical insurance or disability request. One or more claims of medical conditions are received from a claimant. Medical evidence queries are generated based on the received claim and based on a disability rules collection. Each query is preferably associated with a rating code of the rules collection. Medical evidence is then collected from the claimant according to the generated queries. A rating report that displays the collected medical evidence is created to assist rating personnel in making a rating decision. The rating report also preferably displays the rating codes associated with the displayed medical evidence.
Still another embodiment relates to a medical disability claims benefits system. It includes a rules mapping component, a knowledge library, a claimant-specific queries creation component and a protocol creation component. The rules mapping component organizes a medical disability rules collection into at least a first plurality of general medical evidence queries. The knowledge library stores the first plurality of general medical evidence queries. The claimant-specific query creation selects from the first plurality of general queries stored in the knowledge library a second plurality of claimant-specific medical evidence queries based on at least one claimed medical condition of a claimant. The protocol creation component creates at least one data collection protocol based on the second plurality of queries. The system can also include a report creation component, which receives medical evidence collected according to the at least one data collection protocol, and displays at least a portion of the received medical evidence. These components can be implemented in computer instructions. They can be combined or separated into fewer or more components.
For purposes of summarizing the invention, certain aspects, advantages and novel features of the invention have been described herein. Of course, it is to be understood that not necessarily all such aspects, advantages or features will be embodied in any particular embodiment of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates the general overview of one embodiment of a disability benefits claims system.
FIG. 2 illustrates one embodiment of an arrangement of modules and sub-modules.
FIG. 3 illustrates one embodiment of an arrangement of categories and sub-categories.
FIG. 4 illustrates one embodiment of a process of organizing rules collection into a knowledge library.
FIG. 5 illustrates one embodiment of a process of generating claimant-specific medical evidence queries.
FIG. 6 illustrates one embodiment of a data entry form for claimed medical conditions.
FIGS. 7A-7D illustrate one embodiment of a medical provider's exam protocol.
FIGS. 8A-8E illustrate one embodiment of a claimant questionnaire.
FIGS. 9A-9B illustrate one embodiment of a narrative medical report.
FIG. 10 illustrates one embodiment of a diagnostic code summary medical report.
FIGS. 11A-11H illustrate one embodiment of a rating report.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
To better illustrate the invention, certain embodiments of the invention are described below in connection with the drawings. It should be understood that the scope of the invention is not limited by these embodiments but defined by the claims.
Overview of Disability Benefits Claims System
FIG. 1 illustrates the general overview of one embodiment of a disability benefits claims system 1. The rules collection 112 for the insurance or disability program and pertinent medical knowledge 114 are organized by a FID mapping component 116 into a knowledge library 1. Based on the claimed medical conditions 120 from a claimant and based on the rules collection or medical knowledge stored in the knowledge library 118, the claimant-specific query creation module 122 creates claimant-specific medical evidence queries.
The queries are then separated into medical record queries 124 for medical records, and exam queries 126 for physical exams and laboratory tests. The medical record queries 124 for medical records are used by the clerk's protocol creation component 134 to create a clerk's data collection protocol to collect the required data from medical records. The exam queries 126 for exams and tests are used by the medical provider's exam protocol creation component 136 to create a medical provider's data collection protocol to assist a physician, nurse or technician in conducting physical exams or laboratory tests. The component 136 may also use the exam queries 126 to create a claimant's questionnaire to be answered by the claimant. The medical report creation component 142 uses the medical evidence collected from exams, tests, claimant questionnaire and medical records to create a medical report. The rating report creation component 152 creates a rating report to assist rating personnel in adjudicating the claims. The collected medical evidence can be stored in a claimant database 160.
Organizing Rules Collection into Knowledge Library
Still referring to FIG. 1, component 112 represents the rules collection for the insurance or disability program, typically embodied in rating books, legislation, administrative decisions and case law. Component 114 represents pertinent medical knowledge, such as instructions to a physician, lab technician or nurse for performing a physical exam or laboratory test. The rules collection and medical knowledge are organized by a FID mapping component 116 into FIDs and stored in a knowledge library component 118.
In a preferred embodiment, every unit of data that may be required by the rules collection for making a rating decision is identified by a field identification number (FID). Examples of FID data fields include a “patient name” field, a “heart rate” field, a “impaired limb motion range” field, and so forth. Each general medical evidence query is identified by a FID. A general medical evidence query corresponds to a medical evidence requirement specified by the rules collection or by medical knowledge. A claimant-specific medical evidence query is generated from the general medical evidence queries and based on the claimant's claimed medical conditions. Claimant-specific queries are described in the subsection titled “Generating claimant-specific medical evidence queries”.
In a preferred arrangement, each FID includes a category code, a rating code and a data query code, separated by the underline symbol “_”. For example, a FID can take the form of “H047_SM500_T001”. The category code “H047” identifies the FID to a category of queries concerning the right knee. The rating code “SM500” identifies the FID to a particular rating code for musculoskeletal injuries in a rating book. The data query code “T001” identifies the FID to the data query “What is the range of motion?.” In another example, the FID mapping number is “TK10_TS6600_T001”. The category code “TK10” represents a category of queries concerning bronchitis. The rating code “TS6600” represents a rating book rating code “6600”. The data query code “T001” represents the query “What is the FEV1 value?.” A query text table stores the data query codes and the query text for each of the data query codes. The table may also store a long instruction text for each data query code as an instruction or explanation. The stored query text and long instruction text can be later displayed in a medical provider's exam protocol, claimant questionnaire, clerk's data collection protocol, medical report or rating report.
A FID can take other forms. For example, in a relational database arrangement, a rating code table can store the rating code for each data query code, and a category code table can store the category code for each data query code. Therefore a FID need only include a data query code, and the rating code and category code for the FID can be identified by referencing the rating code table and the category code table. In an object-oriented arrangement, a FID can be an object that includes a data query object field, a rating code object field and a category code object field.
The FID mapping component 116 organizes the rules collection into a plurality of FIDs. For example, for a rating code that identifies diabetes in a V.A. rules collection, the component 116 creates a plurality of FIDs, with each FID identifying a unit of medical evidence required for making a rating decision on the diabetes claim. Each FID preferably includes a category code, the V.A. rating code that identifies diabetes, and a data query code. For example, one FID includes a data query code representing the data query “Have you served in the Vietnam War?” because V.A. rules assume that Vietnam veterans' diabetes conditions are caused by exposure to Agent Orange. As described above, the data query code may be further associated with a long instruction text “If claimant has served in Vietnam and suffers from diabetes, assume that service connection exists.”
Arrangement of Modules and Sub-Modules, Categories and Sub-Categories
FIG. 2 illustrates one embodiment of a disability benefits claims systems 100 that includes modules and sub-modules. A rating book typically classifies medical conditions into disease systems, also called body systems. Typical disease systems may include the cardiovascular system, the respiratory system, infectious diseases, and so forth. Some rating books classify a disease system into one or more sub-disease systems or sub-body systems. For example, a “cardiovascular disease system” may include sub-disease systems such as myocardial-infarction sub-disease system, arrhythmia sub-disease system, and so forth. A sub-disease system is typically unique to one disease system and is not shared by multiple disease systems.
As shown in FIG. 2, each sub-disease system is mapped to one or more modules of the disability benefits claims system 1. A module represents a function within the sub-disease system. For example, the lung sub-disease system can be mapped to a “history of symptoms” module, a “history of general health” module, a “physical examination of the lungs” module, and so forth. In one embodiment, sub-disease systems can share common modules. In one embodiment, modules are assigned priority numbers that identify a priority order among the modules.
Each module can include one or more sub-modules. For example, a “vital signs” sub-module can include data about the height, weight, pulse, and blood pressure of the claimant. A sub-module includes one or more FIDs. Modules can share common sub-modules. For example, the “vital signs” sub-module can be shared by multiple modules because vital signs information is needed for the diagnosis of many diseases and conditions. In one embodiment, sub-modules are assigned priority numbers that identify a priority order among the sub-modules.
A sub-module includes one or more FIDs. For example, the “vital signs” sub-module includes a “height” FID, a “weight” FID, a “pulse” FID and a “blood pressure” FID. In a preferred embodiment, each FID belongs to only one sub-module. In one arrangement, each FID includes a sub-module code that identifies the sub-module of the FID. In another embodiment, a sub-module table in the knowledge library 118 stores the FIDs for each sub-module.
In other embodiments, modules and sub-modules are not introduced. Each rating code and its general medical evidence queries directly correspond to a collection of FIDs. The FID collections for two rating codes may share one or more FIDs.
Referring to FIG. 3, the unique data elements that make up the rules collection are grouped by category and sub-category. The categories and sub-categories preferably relate to classifications in the rating books. For example, categories can include “General”, “Complications”, “Function”, “Symptoms”, “Tests”, and so forth. A category can be further classified into one or more sub-categories. For example, the “Function” category includes the sub-categories “ability” and “restriction”. The “Tests” category can include sub-categories “confirmation,” “essential,” “indication,” and “results.” A sub-category includes one or more FIDs.
FIG. 4 illustrates one embodiment of a process of organizing rules collection into FIDs. From a start block 410, the process proceeds to a block 420 to identify rating codes from the rating books for the disability or insurance program. The process then proceeds to a block 430 to identify data fields within each rating code. Each data field represents a general medical evidence query. Data fields may also be identified based on pertinent medical knowledge, for example the knowledge of a experienced physician that certain medical evidence are needed to make a rating decision for a particular rating code. Data fields may also be identified based on case law and administrative decisions, for example the Deluca case and required “Deluca issues.”
The process then proceeds to a block 440 to group the data fields by category. In another embodiment, data fields are grouped by sub-category. The process proceeds to a block 450, where a FID is assigned to each data field. In a preferred embodiment, a category code, a rating code and a data query code is assigned to each FID. The category code represents the category the data field is grouped into. The rating code represents the rating code for the data field. The data query code represents the data query for the medical evidence query. The process then proceeds to a block 460 to store the FIDs in a knowledge library component 1. The process terminates at an end block 470.
Generating Claimant-Specific Medical Evidence Queries
In FIG. 1, the claimant-specific query creation module 122 receives the claimed medical conditions 120 from the claimant, and creates claimant-specific medical evidence query based on the claims and by referring to the general medical evidence queries stored in the knowledge library 1. FIG. 5 illustrates one embodiment of the query-creation process.
Referring to FIG. 5, the process starts from a start block 510 and proceeds to a block 520, where the query creation component 122 receives one or more claims of medical conditions from the claimant. In one embodiment, the component 122 also receives other information provided by the claimant, for example information such as claimant name, age, gender filled out by the claimant on a data entry form form. FIG. 6 is an example data entry form. It can be filled out by the claimant or by a clerk. The “Special Instructions to the Doctor” section displays special instructions retrieved from the knowledge library 118 for the particular insurance or disability program and displayed as a reminder to the medical provider.
Referring back to FIG. 5, at a block 530, the component 122 identifies the related modules based on the received claims. For example, if the claimed condition is “loss of eyesight,” the component 122 may identify a “physical exam” module and a “neurological exam” module. The relationships of medical conditions and related modules are stored in the knowledge library 1. The component 122 also identifies all sub-modules of the identified modules. If two of the identified modules share common sub-modules, the duplicate sub-modules with the lower priority numbers are removed. From all of the FIDs that belong to the identified modules, the duplicate FIDs can also be removed. In other embodiments, instead of identifying the related modules based on the received claims, the component 122 identifies the related sub-modules, the related categories, or the related sub-categories. In another embodiment, the component 122 directly identifies the related FIDs stored in the knowledge library 118 based on the received claims.
At a block 540 of FIG. 5, the component 122 selects those FIDs in the knowledge library 118 that belong to the identified modules and sub-modules. The selected FIDs form a set of the claimant-specific medical evidence queries. The set can be stored in a variety of formats, for example as a text string with FIDs separated by field delimiters such as colons or semicolons, as a text file with a FID in each line, as a table with each FID as a record, as a series of objects with each FID having a “next FID” pointer that points to the next FID object, and so forth. In one embodiment, the component 122 compares the information already received from the claimant, and fills the related FIDs with such information. For example, if the claimant has provided his or her name, age and gender, the component then fills the related FIDs with the claimant-provided information. The details of filling a FID with collected medical evidence are described below in more detail.
From the block 540, the process proceeds to a block 550, where the component 122 determines which of the generated claimant-specific queries may be satisfied from medical records. In another embodiment, a human operator reviews the generated queries and determines which of the queries may be satisfied from medical records. In other embodiments, instead of determining on a per FID basis, the determination can also be made on a per module, per sub-module, per category or per sub-category basis.
If all generated queries may be obtained from medical records, then the process proceeds to a block 5. Otherwise the process proceeds to a block 560, where the component 122 generates a set of claimant-specific queries to be satisfied from physical exams, claimant questionnaires, or laboratory tests. At the block 570, the component 122 generates a set of queries whose results can be obtained from existing medical records. The claimant-specific queries generated at the block 540 are thus separated into two sets of queries. In another embodiment, the queries generated at the block 540 are separated into three sets: one set of queries to be satisfied from physical exams and claimant questionnaires, another set to be satisfied from laboratory tests, and a third set to be satisfied from medical records.
Referring back to the blocks 530 and 540 of FIG. 5, when the claimant submits claims for multiple conditions, it is possible that some of the modules are identified more than once by the claims. The component 122 searches for duplicate modules and eliminates such duplications. In other embodiments, the component can also search for and eliminate duplications on the sub-module or FID level.
Each module is associated with a priority number stored in the knowledge library 1. In the case where multiple modules are called that examine the same sub-disease system, the duplicate modules with the lower priority numbers are eliminated. In another embodiment, each FID is associated with a priority number stored in the knowledge library 118.
In one embodiment, the generated queries can be updated by a human operator. For example, a medical provider or rating personnel reviews the generated claimant-specific queries and adds, modifies or deletes one or more queries. This allows some flexibility and human control in the system 1. The human operator can also change the order of generated claimant-specific queries determined by the priority numbers.
The component 122 also checks special rules stored in the knowledge library 118 for exceptions and updates. Exceptions and updates are typically caused by changes in legislation, case law, and insurance or disability program rules. For example, special rules that represent the Deluca case decision can be stored in the knowledge library 1. The stored Deluca special rules can be associated with FIDs, categories or modules stored in the knowledge library 1. When the generated claimant-specific queries include a FID associated with a special rule, the special rule is retrieved from the knowledge library 118 and applied to include a special rule instruction with the FID, or to add, modify or remove other claimant-specific queries. The special rule can also change the order of generated claimant-specific queries determined by the priority numbers.
Creating Medical Provider's and Clerk's Data Collection Protocols
Referring back to FIG. 1, based on the generated set of claimant-specific queries for exams, the medical provider's protocol creation component 136 creates a medical provider's data collection protocol, also called a physician's exam protocol. The component 136 may also create a claimant questionnaire based on the claimant-specific queries. Based on the generated set of claimant-specific queries for medical records, the clerk's protocol creation component 134 creates a clerk's data collection protocol.
FIGS. 7A-7D illustrate an example medical provider's data collection protocol. The protocol lists the claimant-specific medical evidence queries to be satisfied from physician exams and laboratory tests. In the embodiment shown in FIGS. 7A-7D, the queries are grouped by category and sub-category. For example, the category “PHYSICIAL EXAMINATION” shown in FIG. 7A includes sub-categories “VITAL SIGNS”, “HEENT”, “EYES”, “SKIN”, “HEART”, and “MUSCULOSKELETAL SYSTEM”. The grouping of categories and sub-categories presents the queries in a user-friendly order to the medical provider.
The medical provider uses the exam protocol to examine the claimant, and preferably enters collected medical evidence into the protocol. In one embodiment, the exam protocol is displayed to the medical provider on the screen of an electronic device such as a computer or a personal digital assistant, and the medical provider enters the collected medical evidence corresponding to each query into the electronic device. In another embodiment, the exam protocol is displayed to the medical provider in a paper report, and the medical provider enters the collected medical evidence on the paper report for a clerk to enter into a computer system.
The medical evidence collected by the medical provider is then stored into the disability claims benefits system 1. In one embodiment, for each generated claim-specific query and its FID, the corresponding medical evidence is simply inserted into the end of the FID. For example, for the FID “H047_SM500_T001” described above, if the medical provider determines that the range of motion is 90 degrees, then the FID becomes “H047_SM500_T001 90”, with the last field within the FID storing the value of the medical evidence. In another embodiment, a table includes a “original FID” field that stores the FID of each query, and a “data value” that stores the medical evidence value of the corresponding FID. Other embodiments can also be implemented.
To replace or to supplement the physician's exam protocol, the component 136 may create a claimant questionnaire for those queries that can be satisfied by collecting answers directly from the claimant. FIGS. 8A-8E illustrate an example claimant questionnaire. The questionnaire can be filled out in paper or electronic form, by the claimant or by a clerk assisting the claimant. The questionnaire displays generated claimant-specific queries that can be satisfied by collecting answers from the claimant. The data entered into the questionnaire is then stored as collected medical evidence corresponding to the displayed queries. The data can be stored with the FIDs as described above, and preferably displayed to the physician for review or verification.
The clerk's data collection protocol displays generated claimant-specific queries that are to be collected from medical records. For each query, the protocol preferably displays an instruction to the clerk, for example “retrieve data from previous x-ray charts.” The instructions can be retrieved from instructions stored in the knowledge library 118 that are associated with stored general medical evidence queries. In one embodiment, the disability benefits claims system is connected to an electronic data storage that stores existing medical records, for example the claimant database 1. The system then automatically searches the storage and collects required medical evidence from the storage. In another embodiment, the system automatically notifies a custodian of medical records via email, voice mail or paper report to search for the medical evidence specified by the queries.
Follow-Up Queries Based on Collected Medical Evidence
The query creation component 122 may create conditional claimant-specific queries. For example, if a required exam reveals an abnormal condition, then additional medical evidence may be required according to the rules collection 112 or according to medical knowledge 1. Such additional medical evidence queries are called conditional queries. The query whose medical evidence may trigger the conditional queries is called a triggering query. A triggering query may be associated with one or more sets of conditional queries. For example, a positive result of a laboratory test for a triggering query requires a first set of conditional queries, and a negative result may require a second set of conditional queries.
In one embodiment, the FID of a triggering query stored in the knowledge library 118 includes a list of the FIDs of the conditional queries. In another embodiment, each query is stored as an object in the knowledge library 118, and a triggering query object includes pointers to point to its conditional query objects. In yet another embodiment, the FID of a triggering query includes a flag code to indicate it is a triggering query. A triggering query table includes a first field that stores the FID of a triggering query and a second field that stores the FIDs of the corresponding conditional queries. In each embodiment, the knowledge library 118 may also store a triggering rule that indicates under what conditions the conditional queries are needed, for example “when the triggering query returns a positive test result” or “when the triggering query's medical evidence is not available.”
Regardless of the storage embodiments, when the claimant-specific query creation component 122 generates a triggering query as a claimant-specific query, the conditional queries for the triggering query are preferably also generated as claimant-specific queries. The protocol creation components 134 and 136 identifies a triggering query, and preferably displays its corresponding conditional queries immediately following the triggering query. The medical provider's exam protocol, clerk's data collection protocol and claimant's questionnaire preferably include instructions to explain the triggering rules, for example “if this test result is positive, then answer the following questions.”
The conditional queries can be displayed after the medical evidence for the triggering query is collected. For example, a medical provider's exam protocol is displayed to the medical provider on the screen of an electronic device, and the medical provider enters collected medical evidence into the electronic device. As the medical provider enters the medical evidence for a triggering query into the electronic device, the system 100 compares the entered medical evidence with the triggering query's triggering rule stored in the knowledge library 118, and displays the conditional queries according to the triggering rule. If the conditional queries are to be collected from physical exams, they are displayed on the electronic device or on an additional paper report. The conditional queries can also be displayed on a claimant questionnaire or clerk's data collection protocol, in electronic or paper form.
Creating Medical Report
Referring back to FIG. 1, after medical evidence is collected from physical examinations, laboratory tests, medical records and claimant questionnaire, the collected medical evidence is used by a medical report creation component 142 to create a medical report. FIGS. 9A-9B and FIG. 10 illustrate two example medical reports. FIGS. 9A-9B illustrate a sample narrative report. It includes collected medical evidence, for example medical history data and other data, in preferably a narrative form.
FIG. 10 illustrates a sample diagnostic code summary report. For a claimed right knee medical condition, the report displays a summary of claimant-specific queries and medical evidences, and corresponding rating codes such as “5010” and “5003”. In one preferred embodiment described above, the FID for each query includes a category code, a rating code and a data query code. The rating code of the FID is thus displayed along with the collected medical evidence of the query. The report thus displays direct relationships of medical conditions, medical evidence and rating codes.
The medical report can be used by medical providers to review the claimant's medical evidence and to familiarize the medical providers with the associated rating codes. The report can also be used by rating personnel to review the claimant's medical evidence and associated rating codes. In some embodiments, medical reports can be used interchangeably with rating reports, which are described below in connection with FIGS. 11A-11H.
Depending on the insurance or disability program, reports of different formats can be generated to conform to the commonly accepted format of the particular program. For example, the medical evidence queries can be grouped by disease system on a report for a first insurance program, and grouped by module on another report for a second disability program.
Creating Rating Report
Referring back to FIG. 1, the rating report creation component 152 creates a rating report to assist rating personnel to adjudicate the insurance or disability requests of the claimant. FIGS. 11A-11H illustrate an example rating report, also called a rating decision toolkit.
In one embodiment, the rating report creation component 152 also recommends a rating decision to the rating personnel. The rating decision can be generated based on a set of mathematical formulas, a rule-based system, an expert system, a self-learning neural network, a fuzzy logic system, and so forth. The recommended rating decision can be generated in the form of a numerical value representing a disability percentage, a numerical value representing the insurance benefits dollar amount, a binary value representing a decision to grant or deny an insurance request, and so forth. As shown in FIG. 11C, for the rating code “5259”, the component 152 recommends a V.A. rating of “10”, i.e., a disability percentage of 10%. FIG. 11G displays a summary of all rating codes and corresponding recommended disability percentages, and a recommended combined disability percentage. The rating personnel can review the rating report and accept, reject or modify the recommended ratings.
The disclosed disability claims benefits system 100 can be implemented in a variety of computer languages, commercial applications and operating platforms. For example, the system can be implement in whole or in part in Visual Basis, C, SQL, and so forth.
Certain aspects, advantages and novel features of the invention have been described herein. Of course, it is to be understood that not necessarily all such aspects, advantages or features will be embodied in any particular embodiment of the invention. The embodiments discussed herein are provided as examples of the invention, and are subject to additions, alterations and adjustments. Therefore, the scope of the invention should be defined by the following claims.

Claims (30)

What is claimed is:
1. A computer-implemented method of assisting medical evidence collection for adjudication of a medical disability request, the method comprising:
receiving, from a claimant, at least one claim specifying a medical condition;
selecting, from a predetermined set of medical evidence queries, a plurality of claim-type specific medical evidence queries based on and limited by the at least one claim and based on and limited by a disability rating rules collection;
receiving medical evidence data responsive to the selected medical evidence queries; and
determining, by a computer, a degree of disability based on the collected medical evidence data and the disability rating rules collection;
wherein the selected plurality of medical evidence queries is limited to queries, from the predetermined set of medical evidence queries, that are, based on the disability rating rules collection, specific to the claim, specific to the condition, and specific to the claim type.
2. The method of claim 1, wherein the receiving medical evidence data comprises receiving medical evidence data by conducting a physical examination of the claimant.
3. The method of claim 1, wherein the receiving medical evidence data comprises receiving medical evidence data by conducting a laboratory test of the claimant.
4. The method of claim 1, wherein the receiving medical evidence data comprises receiving medical evidence data from medical records of the claimant.
5. The method of claim 1, wherein the receiving medical evidence data comprises receiving medical history data of the claimant.
6. The method of claim 1, wherein the degree of disability comprises a recommended disability percentage.
7. The method of claim 1, further comprising associating each of the selected medical evidence queries with a rating code of the disability rating rules collection.
8. The method of claim 1, further comprising displaying at least a portion of the selected queries in a medical provider's exam protocol.
9. The method of claim 8, wherein the displaying at least a portion of the selected queries in a medical provider's exam protocol comprises arranging the displayed queries according to modules.
10. The method of claim 8, wherein the displaying at least a portion of the selected queries in a medical provider's exam protocol comprises arranging the displayed queries according to classifications in the disability rating rules collection.
11. The method of claim 1, further comprising displaying at least a portion of the selected queries in an assistant's data collection protocol.
12. The method of claim 1, further comprising displaying at least a portion of the selected queries in a claimant questionnaire.
13. The method of claim 1, further comprising eliminating duplicate selected medical evidence queries.
14. The method of claim 1, wherein the disability rating rules collection comprises a federal law.
15. The method of claim 1, wherein the disability rating rules collection comprises a court case law.
16. The method of claim 1, wherein the disability rating rules collection comprises an administrative decision.
17. The method of claim 1, further comprising:
separating the selected medical evidence queries into at least a first set of queries and a second set of queries;
displaying the first set of queries in a medical provider's exam protocol; and
displaying the second set of queries in an assistant's data collection protocol.
18. The method of claim 17, wherein the first set and the second set of queries include a duplicate query, the method further comprising:
receiving medical evidence data for the duplicate query collected based on the assistant's data collection protocol; and
removing the duplicate query from the medical provider's exam protocol.
19. The method of claim 1, wherein the plurality of medical evidence queries is claimant-specific and claim-specific.
20. The method of claim 19, wherein the selected medical information queries vary by the claimant, vary by the claim, and vary by the claim-type.
21. A computer-implemented method of assisting adjudication of a medical disability request, the method comprising:
receiving, from a claimant, at least one claim specifying a medical condition;
selecting, from a predetermined set of medical evidence queries, a plurality of claim-type specific medical evidence queries based on and limited by the at least one claim and based on and limited by a disability rating rules collection;
limiting the selected queries, from the predetermined set of medical evidence queries, to queries that are, based on the disability rating rules collection, specific to the claim, specific to the condition, and specific to the claim type;
receiving medical evidence responsive to the generated medical evidence queries;
determining, by a computer, a degree of disability based on the collected medical evidence data and the disability rating rules collection; and
displaying, in a rating report, at least a portion of the collected medical evidence and the degree of disability.
22. The method of claim 21, wherein each of the selected queries is associated with a rating code of the disability rating rules collection, and wherein displaying in a rating report further comprises displaying in the rating report rating codes associated with the displayed medical evidence.
23. The method of claim 21, wherein the plurality of medical evidence queries is claimant-specific and claim-specific.
24. The method of claim 23, wherein the selected medical information queries vary by the claimant, vary by the claim, and vary by the claim-type.
25. A medical disability claims benefits system comprising:
a rules mapping component, embodied in a computer readable medium, that organizes a medical disability rating rules collection into at least a first plurality of general medical evidence queries;
a claimant-specific query creation component, embodied in a computer readable medium, that selects, from the first plurality of general queries a second plurality of claim-type specific medical evidence queries based on and limited by a claimed medical condition of a claimant and based on and limited by the disability rating rules collection;
a protocol creation component, embodied in a computer readable medium, that creates a query protocol based on the second plurality of queries; and
a report creation component that receives medical evidence responsive to the at least one query protocol, and adapted to determine a degree of disability based on the collected medical evidence data and the medical disability rating rules collection;
wherein the second plurality of medical evidence queries is limited to queries, from the first plurality of medical evidence queries, that are, based on the disability rating rules collection, specific to the claim, specific to the condition, and specific to the claim type.
26. The system of claim 25, wherein the rules mapping component further associates each of the first plurality of queries with a rating code of the disability rating rules collection.
27. The system of claim 25, wherein the first plurality of general medical evidence queries includes a triggering query and one or more conditional queries associated with the triggering query.
28. The system of claim 25, wherein the claimant-specific query creation component eliminates duplicate queries from the second plurality of medical evidence queries.
29. The system of claim 25, wherein the plurality of medical evidence queries is claimant-specific and claim-specific.
30. The system of claim 29, wherein the selected medical information queries vary by the claimant, vary by the claim, and vary by the claim-type.
US12/603,561 2001-10-24 2009-10-21 Automated processing of medical data for disability rating determinations Expired - Fee Related US8527303B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US12/603,561 US8527303B2 (en) 2001-10-24 2009-10-21 Automated processing of medical data for disability rating determinations

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US34599801P 2001-10-24 2001-10-24
US34466301P 2001-10-25 2001-10-25
US10/279,759 US7630911B2 (en) 2001-10-24 2002-10-23 Method of automated processing of medical data for insurance and disability determinations
US12/603,561 US8527303B2 (en) 2001-10-24 2009-10-21 Automated processing of medical data for disability rating determinations

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US10/279,759 Continuation US7630911B2 (en) 2001-10-24 2002-10-23 Method of automated processing of medical data for insurance and disability determinations

Publications (2)

Publication Number Publication Date
US20100106520A1 US20100106520A1 (en) 2010-04-29
US8527303B2 true US8527303B2 (en) 2013-09-03

Family

ID=37743657

Family Applications (4)

Application Number Title Priority Date Filing Date
US10/279,759 Expired - Fee Related US7630911B2 (en) 2001-10-24 2002-10-23 Method of automated processing of medical data for insurance and disability determinations
US11/155,908 Expired - Fee Related US7630913B2 (en) 2001-10-24 2005-06-20 Automated processing of medical data for disability rating determinations
US12/603,561 Expired - Fee Related US8527303B2 (en) 2001-10-24 2009-10-21 Automated processing of medical data for disability rating determinations
US12/603,550 Expired - Fee Related US7949550B2 (en) 2001-10-24 2009-10-21 Automated processing of medical data for disability rating determinations

Family Applications Before (2)

Application Number Title Priority Date Filing Date
US10/279,759 Expired - Fee Related US7630911B2 (en) 2001-10-24 2002-10-23 Method of automated processing of medical data for insurance and disability determinations
US11/155,908 Expired - Fee Related US7630913B2 (en) 2001-10-24 2005-06-20 Automated processing of medical data for disability rating determinations

Family Applications After (1)

Application Number Title Priority Date Filing Date
US12/603,550 Expired - Fee Related US7949550B2 (en) 2001-10-24 2009-10-21 Automated processing of medical data for disability rating determinations

Country Status (1)

Country Link
US (4) US7630911B2 (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11461848B1 (en) 2015-01-14 2022-10-04 Alchemy Logic Systems, Inc. Methods of obtaining high accuracy impairment ratings and to assist data integrity in the impairment rating process
US11625687B1 (en) 2018-10-16 2023-04-11 Alchemy Logic Systems Inc. Method of and system for parity repair for functional limitation determination and injury profile reports in worker's compensation cases
US11848109B1 (en) 2019-07-29 2023-12-19 Alchemy Logic Systems, Inc. System and method of determining financial loss for worker's compensation injury claims
US11854700B1 (en) 2016-12-06 2023-12-26 Alchemy Logic Systems, Inc. Method of and system for determining a highly accurate and objective maximum medical improvement status and dating assignment
US11853973B1 (en) 2016-07-26 2023-12-26 Alchemy Logic Systems, Inc. Method of and system for executing an impairment repair process

Families Citing this family (47)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6632429B1 (en) 1999-12-17 2003-10-14 Joan M. Fallon Methods for treating pervasive development disorders
US20070053895A1 (en) 2000-08-14 2007-03-08 Fallon Joan M Method of treating and diagnosing parkinsons disease and related dysautonomic disorders
US8030002B2 (en) 2000-11-16 2011-10-04 Curemark Llc Methods for diagnosing pervasive development disorders, dysautonomia and other neurological conditions
US7899688B2 (en) 2001-12-31 2011-03-01 Genworth Financial, Inc. Process for optimization of insurance underwriting suitable for use by an automated system
US8005693B2 (en) 2001-12-31 2011-08-23 Genworth Financial, Inc. Process for determining a confidence factor for insurance underwriting suitable for use by an automated system
US7895062B2 (en) 2001-12-31 2011-02-22 Genworth Financial, Inc. System for optimization of insurance underwriting suitable for use by an automated system
US7844477B2 (en) 2001-12-31 2010-11-30 Genworth Financial, Inc. Process for rule-based insurance underwriting suitable for use by an automated system
US7818186B2 (en) 2001-12-31 2010-10-19 Genworth Financial, Inc. System for determining a confidence factor for insurance underwriting suitable for use by an automated system
US7844476B2 (en) 2001-12-31 2010-11-30 Genworth Financial, Inc. Process for case-based insurance underwriting suitable for use by an automated system
US8793146B2 (en) 2001-12-31 2014-07-29 Genworth Holdings, Inc. System for rule-based insurance underwriting suitable for use by an automated system
US7801748B2 (en) 2003-04-30 2010-09-21 Genworth Financial, Inc. System and process for detecting outliers for insurance underwriting suitable for use by an automated system
US7383239B2 (en) 2003-04-30 2008-06-03 Genworth Financial, Inc. System and process for a fusion classification for insurance underwriting suitable for use by an automated system
US7813945B2 (en) 2003-04-30 2010-10-12 Genworth Financial, Inc. System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system
US8566125B1 (en) 2004-09-20 2013-10-22 Genworth Holdings, Inc. Systems and methods for performing workflow
US7860812B2 (en) * 2005-03-02 2010-12-28 Accenture Global Services Limited Advanced insurance record audit and payment integrity
US20080058282A1 (en) 2005-08-30 2008-03-06 Fallon Joan M Use of lactulose in the treatment of autism
US7716147B2 (en) * 2006-10-23 2010-05-11 Health Care Information Services Llc Real-time predictive computer program, model, and method
US7707130B2 (en) * 2006-10-23 2010-04-27 Health Care Information Services Llc Real-time predictive computer program, model, and method
WO2009111287A2 (en) * 2008-02-29 2009-09-11 Crowe Paradis Holding Company Methods ans systems for automated, predictive modeling of the outcome of benefits claims
US8658163B2 (en) 2008-03-13 2014-02-25 Curemark Llc Compositions and use thereof for treating symptoms of preeclampsia
US8084025B2 (en) 2008-04-18 2011-12-27 Curemark Llc Method for the treatment of the symptoms of drug and alcohol addiction
US8489413B1 (en) * 2008-05-12 2013-07-16 Disability Reporting Services, Inc. System and method for facilitating applications for disability benefits
US9320780B2 (en) 2008-06-26 2016-04-26 Curemark Llc Methods and compositions for the treatment of symptoms of Williams Syndrome
US20090324730A1 (en) * 2008-06-26 2009-12-31 Fallon Joan M Methods and compositions for the treatment of symptoms of complex regional pain syndrome
PL2318035T3 (en) 2008-07-01 2019-10-31 Curemark Llc Methods and compositions for the treatment of symptoms of neurological and mental health disorders
US10776453B2 (en) * 2008-08-04 2020-09-15 Galenagen, Llc Systems and methods employing remote data gathering and monitoring for diagnosing, staging, and treatment of Parkinsons disease, movement and neurological disorders, and chronic pain
US20100092447A1 (en) 2008-10-03 2010-04-15 Fallon Joan M Methods and compositions for the treatment of symptoms of prion diseases
EP2373791B1 (en) 2009-01-06 2016-03-30 Curelon LLC Compositions comprising protease, amylase and lipase for use in the treatment of staphylococcus aureus infections
KR20170005191A (en) 2009-01-06 2017-01-11 큐어론 엘엘씨 Compositions and methods for the treatment or the prevention oral infections by e. coli
US9056050B2 (en) 2009-04-13 2015-06-16 Curemark Llc Enzyme delivery systems and methods of preparation and use
WO2011050135A1 (en) 2009-10-21 2011-04-28 Curemark Llc Methods and compositions for the prevention and treatment of influenza
US9558520B2 (en) * 2009-12-31 2017-01-31 Hartford Fire Insurance Company System and method for geocoded insurance processing using mobile devices
US8805707B2 (en) 2009-12-31 2014-08-12 Hartford Fire Insurance Company Systems and methods for providing a safety score associated with a user location
US20110257993A1 (en) * 2010-03-17 2011-10-20 Qtc Management, Inc. Automated association of rating diagnostic codes for insurance and disability determinations
MX362974B (en) 2011-04-21 2019-02-28 Curemark Llc Compounds for the treatment of neuropsychiatric disorders.
US10340034B2 (en) 2011-12-30 2019-07-02 Elwha Llc Evidence-based healthcare information management protocols
US10528913B2 (en) 2011-12-30 2020-01-07 Elwha Llc Evidence-based healthcare information management protocols
US20130173298A1 (en) 2011-12-30 2013-07-04 Elwha LLC, a limited liability company of State of Delaware Evidence-based healthcare information management protocols
US10475142B2 (en) 2011-12-30 2019-11-12 Elwha Llc Evidence-based healthcare information management protocols
US10679309B2 (en) 2011-12-30 2020-06-09 Elwha Llc Evidence-based healthcare information management protocols
US10552581B2 (en) 2011-12-30 2020-02-04 Elwha Llc Evidence-based healthcare information management protocols
US10559380B2 (en) 2011-12-30 2020-02-11 Elwha Llc Evidence-based healthcare information management protocols
US10350278B2 (en) 2012-05-30 2019-07-16 Curemark, Llc Methods of treating Celiac disease
US9514410B2 (en) 2013-04-30 2016-12-06 Nuesoft Technologies, Inc. System and method for identifying and condensing similar and/or analogous information requests and/or responses
US9922037B2 (en) 2015-01-30 2018-03-20 Splunk Inc. Index time, delimiter based extractions and previewing for use in indexing
CN111754196A (en) * 2020-06-29 2020-10-09 苏州美能华智能科技有限公司 Configuration method, device and storage medium for application process of assistant tool for disability
US11541009B2 (en) 2020-09-10 2023-01-03 Curemark, Llc Methods of prophylaxis of coronavirus infection and treatment of coronaviruses

Citations (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5367675A (en) 1991-12-13 1994-11-22 International Business Machines Corporation Computer automated system and method for optimizing the processing of a query in a relational database system by merging subqueries with the query
US5613072A (en) 1991-02-06 1997-03-18 Risk Data Corporation System for funding future workers compensation losses
US5911132A (en) * 1995-04-26 1999-06-08 Lucent Technologies Inc. Method using central epidemiological database
US6003007A (en) 1996-03-28 1999-12-14 Dirienzo; Andrew L. Attachment integrated claims system and operating method therefor
US6049794A (en) 1997-12-09 2000-04-11 Jacobs; Charles M. System for screening of medical decision making incorporating a knowledge base
US6108665A (en) 1997-07-03 2000-08-22 The Psychological Corporation System and method for optimizing behaviorial health care collection
US6263330B1 (en) 1998-02-24 2001-07-17 Luc Bessette Method and apparatus for the management of data files
US20010041992A1 (en) 2000-03-10 2001-11-15 Medorder, Inc. Method and system for accessing healthcare information using an anatomic user interface
US20010044735A1 (en) 2000-04-27 2001-11-22 Colburn Harry S. Auditing and monitoring system for workers' compensation claims
US20020035486A1 (en) 2000-07-21 2002-03-21 Huyn Nam Q. Computerized clinical questionnaire with dynamically presented questions
US20020046346A1 (en) 1996-09-27 2002-04-18 Evans Jae A. Electronic medical records system
US20020046199A1 (en) 2000-08-03 2002-04-18 Unicru, Inc. Electronic employee selection systems and methods
US20020069089A1 (en) 2000-11-30 2002-06-06 Nupath Solutions (Cincinnati), Ltd. Method for case management of workplace-related injuries
US20020091550A1 (en) 2000-06-29 2002-07-11 White Mitchell Franklin System and method for real-time rating, underwriting and policy issuance
US6434531B1 (en) 1995-02-28 2002-08-13 Clinicomp International, Inc. Method and system for facilitating patient care plans
US20020138306A1 (en) 2001-03-23 2002-09-26 John Sabovich System and method for electronically managing medical information
US6470319B1 (en) 1999-06-25 2002-10-22 Community Corrections Improvement Association Data processing system for determining case management plan for criminal offender
US6581038B1 (en) 1999-03-15 2003-06-17 Nexcura, Inc. Automated profiler system for providing medical information to patients
US6604080B1 (en) 1991-10-30 2003-08-05 B&S Underwriters, Inc. Computer system and methods for supporting workers' compensation/employers liability insurance
US20030200123A1 (en) * 2001-10-18 2003-10-23 Burge John R. Injury analysis system and method for insurance claims
US6738784B1 (en) 2000-04-06 2004-05-18 Dictaphone Corporation Document and information processing system
US20040122708A1 (en) 2002-12-18 2004-06-24 Avinash Gopal B. Medical data analysis method and apparatus incorporating in vitro test data
US20040122707A1 (en) 2002-12-18 2004-06-24 Sabol John M. Patient-driven medical data processing system and method
US20040122704A1 (en) 2002-12-18 2004-06-24 Sabol John M. Integrated medical knowledge base interface system and method
US20040122705A1 (en) 2002-12-18 2004-06-24 Sabol John M. Multilevel integrated medical knowledge base system and method
US20040141661A1 (en) 2002-11-27 2004-07-22 Hanna Christopher J. Intelligent medical image management system
US20050033773A1 (en) 1998-04-01 2005-02-10 Cyberpulse, L.L.C. Method and system for generation of medical reports from data in a hierarchically-organized database
US20050256744A1 (en) 2004-05-13 2005-11-17 Ulf Rohde System for accessing employee-related occupational healthcare information
US6988088B1 (en) 2000-10-17 2006-01-17 Recare, Inc. Systems and methods for adaptive medical decision support
US7191451B2 (en) 2000-12-27 2007-03-13 Fujitsu Limited Medical system with a management software, database, and a network interface to protect patient information from unauthorized personnel
US7260480B1 (en) 2003-04-07 2007-08-21 Health Hero Network, Inc. Method and system for integrating feedback loops in medical knowledge development and healthcare management
US7870011B2 (en) 2001-10-24 2011-01-11 Qtc Management, Inc. Automated processing of electronic medical data for insurance and disability determinations

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4977584B2 (en) * 2007-11-27 2012-07-18 日本車輌製造株式会社 Pile driver

Patent Citations (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5613072A (en) 1991-02-06 1997-03-18 Risk Data Corporation System for funding future workers compensation losses
US6604080B1 (en) 1991-10-30 2003-08-05 B&S Underwriters, Inc. Computer system and methods for supporting workers' compensation/employers liability insurance
US5367675A (en) 1991-12-13 1994-11-22 International Business Machines Corporation Computer automated system and method for optimizing the processing of a query in a relational database system by merging subqueries with the query
US6434531B1 (en) 1995-02-28 2002-08-13 Clinicomp International, Inc. Method and system for facilitating patient care plans
US5911132A (en) * 1995-04-26 1999-06-08 Lucent Technologies Inc. Method using central epidemiological database
US6003007A (en) 1996-03-28 1999-12-14 Dirienzo; Andrew L. Attachment integrated claims system and operating method therefor
US20020046346A1 (en) 1996-09-27 2002-04-18 Evans Jae A. Electronic medical records system
US6108665A (en) 1997-07-03 2000-08-22 The Psychological Corporation System and method for optimizing behaviorial health care collection
US6049794A (en) 1997-12-09 2000-04-11 Jacobs; Charles M. System for screening of medical decision making incorporating a knowledge base
US6263330B1 (en) 1998-02-24 2001-07-17 Luc Bessette Method and apparatus for the management of data files
US20050033773A1 (en) 1998-04-01 2005-02-10 Cyberpulse, L.L.C. Method and system for generation of medical reports from data in a hierarchically-organized database
US6581038B1 (en) 1999-03-15 2003-06-17 Nexcura, Inc. Automated profiler system for providing medical information to patients
US6470319B1 (en) 1999-06-25 2002-10-22 Community Corrections Improvement Association Data processing system for determining case management plan for criminal offender
US20010041992A1 (en) 2000-03-10 2001-11-15 Medorder, Inc. Method and system for accessing healthcare information using an anatomic user interface
US6738784B1 (en) 2000-04-06 2004-05-18 Dictaphone Corporation Document and information processing system
US20010044735A1 (en) 2000-04-27 2001-11-22 Colburn Harry S. Auditing and monitoring system for workers' compensation claims
US20020091550A1 (en) 2000-06-29 2002-07-11 White Mitchell Franklin System and method for real-time rating, underwriting and policy issuance
US20020035486A1 (en) 2000-07-21 2002-03-21 Huyn Nam Q. Computerized clinical questionnaire with dynamically presented questions
US20020046199A1 (en) 2000-08-03 2002-04-18 Unicru, Inc. Electronic employee selection systems and methods
US6988088B1 (en) 2000-10-17 2006-01-17 Recare, Inc. Systems and methods for adaptive medical decision support
US20020069089A1 (en) 2000-11-30 2002-06-06 Nupath Solutions (Cincinnati), Ltd. Method for case management of workplace-related injuries
US7191451B2 (en) 2000-12-27 2007-03-13 Fujitsu Limited Medical system with a management software, database, and a network interface to protect patient information from unauthorized personnel
US20020138306A1 (en) 2001-03-23 2002-09-26 John Sabovich System and method for electronically managing medical information
US20030200123A1 (en) * 2001-10-18 2003-10-23 Burge John R. Injury analysis system and method for insurance claims
US7870011B2 (en) 2001-10-24 2011-01-11 Qtc Management, Inc. Automated processing of electronic medical data for insurance and disability determinations
US20040141661A1 (en) 2002-11-27 2004-07-22 Hanna Christopher J. Intelligent medical image management system
US20040122708A1 (en) 2002-12-18 2004-06-24 Avinash Gopal B. Medical data analysis method and apparatus incorporating in vitro test data
US20040122707A1 (en) 2002-12-18 2004-06-24 Sabol John M. Patient-driven medical data processing system and method
US20040122704A1 (en) 2002-12-18 2004-06-24 Sabol John M. Integrated medical knowledge base interface system and method
US20040122705A1 (en) 2002-12-18 2004-06-24 Sabol John M. Multilevel integrated medical knowledge base system and method
US7260480B1 (en) 2003-04-07 2007-08-21 Health Hero Network, Inc. Method and system for integrating feedback loops in medical knowledge development and healthcare management
US20050256744A1 (en) 2004-05-13 2005-11-17 Ulf Rohde System for accessing employee-related occupational healthcare information

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
"Compensation & Pension Examination-Heart", , Dec. 8, 2004, 3 pages.
"Compensation & Pension Examination—Heart", <http://www.vba.va.gov/bin/21/Benefits/exams/disexm28.htm>, Dec. 8, 2004, 3 pages.
"Compensation & Pension Examination-Respiratory Diseases, Miscellaneous", , Dec. 8, 2004, 3 pages.
"Compensation & Pension Examination—Respiratory Diseases, Miscellaneous", <http://www.vba.va.gov/bIn/21/Benefits/exams/disexm28.htm>, Dec. 8, 2004, 3 pages.
Compensation and Pension Examination Exam #7040 Respiratory Diseases, Miscellaneous, Modified Dec. 15, 1997.
Compensation and Pension Examination Exam #8010 Heart 1997.
Geisel, "Florida plan ponders workers comp benefits based on lost wages", Business Insurance, Chicago, May 1, 1978, p. 3, vol. 12, Issue 9, Proquest LLC (Abstract).

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11461848B1 (en) 2015-01-14 2022-10-04 Alchemy Logic Systems, Inc. Methods of obtaining high accuracy impairment ratings and to assist data integrity in the impairment rating process
US11853973B1 (en) 2016-07-26 2023-12-26 Alchemy Logic Systems, Inc. Method of and system for executing an impairment repair process
US11854700B1 (en) 2016-12-06 2023-12-26 Alchemy Logic Systems, Inc. Method of and system for determining a highly accurate and objective maximum medical improvement status and dating assignment
US11625687B1 (en) 2018-10-16 2023-04-11 Alchemy Logic Systems Inc. Method of and system for parity repair for functional limitation determination and injury profile reports in worker's compensation cases
US11848109B1 (en) 2019-07-29 2023-12-19 Alchemy Logic Systems, Inc. System and method of determining financial loss for worker's compensation injury claims

Also Published As

Publication number Publication date
US20070038478A1 (en) 2007-02-15
US7949550B2 (en) 2011-05-24
US20100106520A1 (en) 2010-04-29
US20100106526A1 (en) 2010-04-29
US7630911B2 (en) 2009-12-08
US7630913B2 (en) 2009-12-08
US20070038479A1 (en) 2007-02-15

Similar Documents

Publication Publication Date Title
US8527303B2 (en) Automated processing of medical data for disability rating determinations
US7870011B2 (en) Automated processing of electronic medical data for insurance and disability determinations
US8725538B2 (en) Automated processing of electronic medical data for insurance and disability determinations
US8090742B2 (en) Patient directed system and method for managing medical information
US5359509A (en) Health care payment adjudication and review system
US5225976A (en) Automated health benefit processing system
Garthe et al. Abbreviated injury scale unification: the case for a unified injury system for global use
US5519607A (en) Automated health benefit processing system
US8160905B2 (en) Method and apparatus for repricing a reimbursement claim against a contract
US20100100395A1 (en) Method for high-risk member identification
US8782050B2 (en) Database and index organization for enhanced document retrieval
US20110314025A1 (en) Database and index organization for enhanced document retrieval
CA2362122A1 (en) Automated profiler system for providing medical information to patients
DE102005012628A1 (en) Processing system for clinical data
US20110257993A1 (en) Automated association of rating diagnostic codes for insurance and disability determinations
Daniel et al. Child abuse screening: Implications of the limited predictive power of abuse discriminants from a controlled family study of pediatric social illness
US11170892B1 (en) Methods and systems for analysis of requests for radiological imaging examinations
US8566275B2 (en) Systems and methods for processing medical data for employment determinations
JP7319301B2 (en) Systems and methods for prioritization and presentation of heterogeneous medical data
Mills Nursing Diagnosis: The importance of a definition
Lavril et al. ARTEL: An expert system in hypertension for the general practitioner
Larcher et al. Incapacitation Data Registry Evolution
O'Kane et al. Generalized protocol driven problem oriented clinical data management and monitoring
Lavril¹ et al. ARTEL: An Expert System in Hypertension for the General Practitioner M. Lavril¹, G. Chatelller2, P. Degoulet¹, D. Sauquet¹, X. Jeunemaltre², J. Ménard2, C. Rovani¹ 1 Medical Informatic Department, Broussais University Hospital, Paris 2Hypertension Department, Broussais University Hospital, Paris
Safir Ophthalmology’s Characteristics as a Special1y, From an Information Science Viewpoint

Legal Events

Date Code Title Description
AS Assignment

Owner name: QTC MANAGEMENT, INC., CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:KAY, LAY K.;REEL/FRAME:030842/0683

Effective date: 20021126

STCF Information on status: patent grant

Free format text: PATENTED CASE

AS Assignment

Owner name: CITIBANK, N.A., DELAWARE

Free format text: SECURITY INTEREST;ASSIGNORS:VAREC, INC.;REVEAL IMAGING TECHNOLOGIES, INC.;ABACUS INNOVATIONS TECHNOLOGY, INC.;AND OTHERS;REEL/FRAME:039809/0634

Effective date: 20160816

Owner name: CITIBANK, N.A., DELAWARE

Free format text: SECURITY INTEREST;ASSIGNORS:VAREC, INC.;REVEAL IMAGING TECHNOLOGIES, INC.;ABACUS INNOVATIONS TECHNOLOGY, INC.;AND OTHERS;REEL/FRAME:039809/0603

Effective date: 20160816

FPAY Fee payment

Year of fee payment: 4

AS Assignment

Owner name: SYSTEMS MADE SIMPLE, INC., NEW YORK

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: OAO CORPORATION, VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: REVEAL IMAGING TECHNOLOGY, INC., VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: QTC MANAGEMENT, INC., CALIFORNIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: SYTEX, INC., VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: VAREC, INC., VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: LEIDOS INNOVATIONS TECHNOLOGY, INC. (F/K/A ABACUS INNOVATIONS TECHNOLOGY, INC.), VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:051855/0222

Effective date: 20200117

Owner name: SYSTEMS MADE SIMPLE, INC., NEW YORK

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

Owner name: QTC MANAGEMENT, INC., CALIFORNIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

Owner name: LEIDOS INNOVATIONS TECHNOLOGY, INC. (F/K/A ABACUS INNOVATIONS TECHNOLOGY, INC.), VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

Owner name: VAREC, INC., VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

Owner name: REVEAL IMAGING TECHNOLOGY, INC., VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

Owner name: SYTEX, INC., VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

Owner name: OAO CORPORATION, VIRGINIA

Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:CITIBANK, N.A., AS COLLATERAL AGENT;REEL/FRAME:052316/0390

Effective date: 20200117

FEPP Fee payment procedure

Free format text: MAINTENANCE FEE REMINDER MAILED (ORIGINAL EVENT CODE: REM.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

LAPS Lapse for failure to pay maintenance fees

Free format text: PATENT EXPIRED FOR FAILURE TO PAY MAINTENANCE FEES (ORIGINAL EVENT CODE: EXP.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

STCH Information on status: patent discontinuation

Free format text: PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362

FP Lapsed due to failure to pay maintenance fee

Effective date: 20210903